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#include <iostream> #include <adept_arrays.h> int main (int argc, const char ** argv) {using namespace adept; Stack stack; // Object to store differential statements aVector x (3); // Independent variables: active vector with 3 elements x << 1.0, 2.0, 3.0; // Fill vector x stack. new_recording (); // Clear any existing differential statements adouble J = cbrt (sum (abs (x * x * x ...
Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...
a variable definition for variable time_keeper of class TimeKeeper, initialized with an anonymous instance of class Timer or a function declaration for a function time_keeper that returns an object of type TimeKeeper and has a single (unnamed) parameter, whose type is a (pointer to a) function [ Note 1 ] taking no input and returning Timer objects.
The curiously recurring template pattern (CRTP) is an idiom, originally in C++, in which a class X derives from a class template instantiation using X itself as a template argument. [1] More generally it is known as F-bound polymorphism , and it is a form of F -bounded quantification .
Then, the backpropagation algorithm is used to find the gradient of the loss function with respect to all the network parameters. Consider an example of a neural network that contains a recurrent layer and a feedforward layer . There are different ways to define the training cost, but the aggregated cost is always the average of the costs of ...
Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992. [1]
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to their partial derivative of the loss function. [1]
In C++, a constructor of a class/struct can have an initializer list within the definition but prior to the constructor body. It is important to note that when you use an initialization list, the values are not assigned to the variable. They are initialized. In the below example, 0 is initialized into re and im. Example: